Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes

arXiv:2406.08758v1 Announce Type: new
Abstract: Generative Adversarial Networks (GANs) have exhibited noteworthy advancements across various applications, including medical imaging. While numerous state-of-the-art Deep Convolutional Neural Network (DCNN) architectures are renowned for their proficient feature extraction, this paper investigates their efficacy in the context of medical image deepfake detection. The primary objective is to effectively distinguish real from tampered or manipulated medical images by employing a comprehensive evaluation of 13 state-of-the-art DCNNs. Performance is assessed across diverse evaluation metrics, encompassing considerations …

abstract adversarial analysis applications architectures art arxiv comparative analysis context convolutional convolutional neural network convolutional neural networks cs.cv deepfake deepfakes detection extraction feature feature extraction gans generative generative adversarial networks image imaging medical medical imaging network networks neural network neural networks paper state type while